Channel charting based beamforming

• URL: http://arxiv.org/abs/2212.12340v2
• Date: Tue, 27 Dec 2022 15:26:32 GMT
• Title: Channel charting based beamforming
• Authors: Luc Le Magoarou (IRT b-com, Hypermedia, INSA Rennes, IETR), Taha Yassine (IRT b-com, Hypermedia, INSA Rennes, IETR), Stephane Paquelet (IRT b-com, Hypermedia, IETR), Matthieu Crussi\`ere (IRT b-com, Hypermedia, INSA Rennes, IETR)
• Abstract summary: Channel charting (CC) is an unsupervised learning method allowing to locate users relative to each other without reference. In this paper, this latent modeling vision is leveraged together with a recently proposed location-based beamforming (LBB) method to show that channel charting can be used for mapping channels in space or frequency.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Channel charting (CC) is an unsupervised learning method allowing to locate users relative to each other without reference. From a broader perspective, it can be viewed as a way to discover a low-dimensional latent space charting the channel manifold. In this paper, this latent modeling vision is leveraged together with a recently proposed location-based beamforming (LBB) method to show that channel charting can be used for mapping channels in space or frequency. Combining CC and LBB yields a neural network resembling an autoencoder. The proposed method is empirically assessed on a channel mapping task whose objective is to predict downlink channels from uplink channels.

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